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#include "precomp.hpp"
#include <iterator>

/****************************************************************************************\
      The code below is implementation of HOG (Histogram-of-Oriented Gradients)
      descriptor and object detection, introduced by Navneet Dalal and Bill Triggs.

      The computed feature vectors are compatible with the
      INRIA Object Detection and Localization Toolkit
      (http://pascal.inrialpes.fr/soft/olt/)
\****************************************************************************************/

namespace cv {

size_t HOGDescriptor::getDescriptorSize() const {
	CV_Assert(blockSize.width % cellSize.width == 0 &&
			  blockSize.height % cellSize.height == 0);
	CV_Assert((winSize.width - blockSize.width) % blockStride.width == 0 &&
			  (winSize.height - blockSize.height) % blockStride.height == 0 );
	return (size_t)nbins *
		   (blockSize.width / cellSize.width) *
		   (blockSize.height / cellSize.height) *
		   ((winSize.width - blockSize.width) / blockStride.width + 1) *
		   ((winSize.height - blockSize.height) / blockStride.height + 1);
}

double HOGDescriptor::getWinSigma() const {
	return winSigma >= 0 ? winSigma : (blockSize.width + blockSize.height) / 8.;
}

bool HOGDescriptor::checkDetectorSize() const {
	size_t detectorSize = svmDetector.size(), descriptorSize = getDescriptorSize();
	return detectorSize == 0 ||
		   detectorSize == descriptorSize ||
		   detectorSize == descriptorSize + 1;
}

void HOGDescriptor::setSVMDetector(const vector<float>& _svmDetector) {
	svmDetector = _svmDetector;
	CV_Assert( checkDetectorSize() );
}

#define CV_TYPE_NAME_HOG_DESCRIPTOR "opencv-object-detector-hog"

bool HOGDescriptor::read(FileNode& obj) {
	if ( !obj.isMap() ) {
		return false;
	}
	FileNodeIterator it = obj["winSize"].begin();
	it >> winSize.width >> winSize.height;
	it = obj["blockSize"].begin();
	it >> blockSize.width >> blockSize.height;
	it = obj["blockStride"].begin();
	it >> blockStride.width >> blockStride.height;
	it = obj["cellSize"].begin();
	it >> cellSize.width >> cellSize.height;
	obj["nbins"] >> nbins;
	obj["derivAperture"] >> derivAperture;
	obj["winSigma"] >> winSigma;
	obj["histogramNormType"] >> histogramNormType;
	obj["L2HysThreshold"] >> L2HysThreshold;
	obj["gammaCorrection"] >> gammaCorrection;

	FileNode vecNode = obj["SVMDetector"];
	if ( vecNode.isSeq() ) {
		vecNode >> svmDetector;
		CV_Assert(checkDetectorSize());
	}
	return true;
}

void HOGDescriptor::write(FileStorage& fs, const String& objName) const {
	if ( !objName.empty() ) {
		fs << objName;
	}

	fs << "{" CV_TYPE_NAME_HOG_DESCRIPTOR
	   << "winSize" << winSize
	   << "blockSize" << blockSize
	   << "blockStride" << blockStride
	   << "cellSize" << cellSize
	   << "nbins" << nbins
	   << "derivAperture" << derivAperture
	   << "winSigma" << getWinSigma()
	   << "histogramNormType" << histogramNormType
	   << "L2HysThreshold" << L2HysThreshold
	   << "gammaCorrection" << gammaCorrection;
	if ( !svmDetector.empty() ) {
		fs << "SVMDetector" << "[:" << svmDetector << "]";
	}
	fs << "}";
}

bool HOGDescriptor::load(const String& filename, const String& objname) {
	FileStorage fs(filename, FileStorage::READ);
	FileNode obj = !objname.empty() ? fs[objname] : fs.getFirstTopLevelNode();
	return read(obj);
}

void HOGDescriptor::save(const String& filename, const String& objName) const {
	FileStorage fs(filename, FileStorage::WRITE);
	write(fs, !objName.empty() ? objName : FileStorage::getDefaultObjectName(filename));
}

void HOGDescriptor::copyTo(HOGDescriptor& c) const {
	c.winSize = winSize;
	c.blockSize = blockSize;
	c.blockStride = blockStride;
	c.cellSize = cellSize;
	c.nbins = nbins;
	c.derivAperture = derivAperture;
	c.winSigma = winSigma;
	c.histogramNormType = histogramNormType;
	c.L2HysThreshold = L2HysThreshold;
	c.gammaCorrection = gammaCorrection;
	c.svmDetector = svmDetector;
}

void HOGDescriptor::computeGradient(const Mat& img, Mat& grad, Mat& qangle,
									Size paddingTL, Size paddingBR) const {
	CV_Assert( img.type() == CV_8U || img.type() == CV_8UC3 );

	Size gradsize(img.cols + paddingTL.width + paddingBR.width,
				  img.rows + paddingTL.height + paddingBR.height);
	grad.create(gradsize, CV_32FC2);  // <magnitude*(1-alpha), magnitude*alpha>
	qangle.create(gradsize, CV_8UC2); // [0..nbins-1] - quantized gradient orientation
	Size wholeSize;
	Point roiofs;
	img.locateROI(wholeSize, roiofs);

	int i, x, y;
	int cn = img.channels();

	Mat_<float> _lut(1, 256);
	const float* lut = &_lut(0, 0);

	if ( gammaCorrection )
		for ( i = 0; i < 256; i++ ) {
			_lut(0, i) = std::sqrt((float)i);
		}
	else
		for ( i = 0; i < 256; i++ ) {
			_lut(0, i) = (float)i;
		}

	AutoBuffer<int> mapbuf(gradsize.width + gradsize.height + 4);
	int* xmap = (int*)mapbuf + 1;
	int* ymap = xmap + gradsize.width + 2;

	const int borderType = (int)BORDER_REFLECT_101;

	for ( x = -1; x < gradsize.width + 1; x++ )
		xmap[x] = borderInterpolate(x - paddingTL.width + roiofs.x,
									wholeSize.width, borderType);
	for ( y = -1; y < gradsize.height + 1; y++ )
		ymap[y] = borderInterpolate(y - paddingTL.height + roiofs.y,
									wholeSize.height, borderType);

	// x- & y- derivatives for the whole row
	int width = gradsize.width;
	AutoBuffer<float> _dbuf(width * 4);
	float* dbuf = _dbuf;
	Mat Dx(1, width, CV_32F, dbuf);
	Mat Dy(1, width, CV_32F, dbuf + width);
	Mat Mag(1, width, CV_32F, dbuf + width * 2);
	Mat Angle(1, width, CV_32F, dbuf + width * 3);

	int _nbins = nbins;
	float angleScale = (float)(_nbins / CV_PI);

	for ( y = 0; y < gradsize.height; y++ ) {
		const uchar* imgPtr = img.data + img.step * ymap[y];
		const uchar* prevPtr = img.data + img.step * ymap[y-1];
		const uchar* nextPtr = img.data + img.step * ymap[y+1];
		float* gradPtr = (float*)grad.ptr(y);
		uchar* qanglePtr = (uchar*)qangle.ptr(y);

		if ( cn == 1 ) {
			for ( x = 0; x < width; x++ ) {
				int x1 = xmap[x];
				dbuf[x] = (float)(lut[imgPtr[xmap[x+1]]] - lut[imgPtr[xmap[x-1]]]);
				dbuf[width + x] = (float)(lut[nextPtr[x1]] - lut[prevPtr[x1]]);
			}
		} else {
			for ( x = 0; x < width; x++ ) {
				int x1 = xmap[x] * 3;
				const uchar* p2 = imgPtr + xmap[x+1] * 3;
				const uchar* p0 = imgPtr + xmap[x-1] * 3;
				float dx0, dy0, dx, dy, mag0, mag;

				dx0 = lut[p2[2]] - lut[p0[2]];
				dy0 = lut[nextPtr[x1+2]] - lut[prevPtr[x1+2]];
				mag0 = dx0 * dx0 + dy0 * dy0;

				dx = lut[p2[1]] - lut[p0[1]];
				dy = lut[nextPtr[x1+1]] - lut[prevPtr[x1+1]];
				mag = dx * dx + dy * dy;

				if ( mag0 < mag ) {
					dx0 = dx;
					dy0 = dy;
					mag0 = mag;
				}

				dx = lut[p2[0]] - lut[p0[0]];
				dy = lut[nextPtr[x1]] - lut[prevPtr[x1]];
				mag = dx * dx + dy * dy;

				if ( mag0 < mag ) {
					dx0 = dx;
					dy0 = dy;
					mag0 = mag;
				}

				dbuf[x] = dx0;
				dbuf[x+width] = dy0;
			}
		}

		cartToPolar( Dx, Dy, Mag, Angle, false );

		for ( x = 0; x < width; x++ ) {
			float mag = dbuf[x+width*2], angle = dbuf[x+width*3] * angleScale - 0.5f;
			int hidx = cvFloor(angle);
			angle -= hidx;
			if ( hidx < 0 ) {
				hidx += _nbins;
			} else if ( hidx >= _nbins ) {
				hidx -= _nbins;
			}
			assert( (unsigned)hidx < (unsigned)_nbins );

			qanglePtr[x*2] = (uchar)hidx;
			hidx++;
			hidx &= hidx < _nbins ? -1 : 0;
			qanglePtr[x*2+1] = (uchar)hidx;
			gradPtr[x*2] = mag * (1.f - angle);
			gradPtr[x*2+1] = mag * angle;
		}
	}
}


struct HOGCache {
	struct BlockData {
		BlockData() : histOfs(0), imgOffset() {}
		int histOfs;
		Point imgOffset;
	};

	struct PixData {
		size_t gradOfs, qangleOfs;
		int histOfs[4];
		float histWeights[4];
		float gradWeight;
	};

	HOGCache();
	HOGCache(const HOGDescriptor* descriptor,
			 const Mat& img, Size paddingTL, Size paddingBR,
			 bool useCache, Size cacheStride);
	virtual ~HOGCache() {};
	virtual void init(const HOGDescriptor* descriptor,
					  const Mat& img, Size paddingTL, Size paddingBR,
					  bool useCache, Size cacheStride);

	Size windowsInImage(Size imageSize, Size winStride) const;
	Rect getWindow(Size imageSize, Size winStride, int idx) const;

	const float* getBlock(Point pt, float* buf);
	virtual void normalizeBlockHistogram(float* histogram) const;

	vector<PixData> pixData;
	vector<BlockData> blockData;

	bool useCache;
	vector<int> ymaxCached;
	Size winSize, cacheStride;
	Size nblocks, ncells;
	int blockHistogramSize;
	int count1, count2, count4;
	Point imgoffset;
	Mat_<float> blockCache;
	Mat_<uchar> blockCacheFlags;

	Mat grad, qangle;
	const HOGDescriptor* descriptor;
};


HOGCache::HOGCache() {
	useCache = false;
	blockHistogramSize = count1 = count2 = count4 = 0;
	descriptor = 0;
}

HOGCache::HOGCache(const HOGDescriptor* _descriptor,
				   const Mat& _img, Size _paddingTL, Size _paddingBR,
				   bool _useCache, Size _cacheStride) {
	init(_descriptor, _img, _paddingTL, _paddingBR, _useCache, _cacheStride);
}

void HOGCache::init(const HOGDescriptor* _descriptor,
					const Mat& _img, Size _paddingTL, Size _paddingBR,
					bool _useCache, Size _cacheStride) {
	descriptor = _descriptor;
	cacheStride = _cacheStride;
	useCache = _useCache;

	descriptor->computeGradient(_img, grad, qangle, _paddingTL, _paddingBR);
	imgoffset = _paddingTL;

	winSize = descriptor->winSize;
	Size blockSize = descriptor->blockSize;
	Size blockStride = descriptor->blockStride;
	Size cellSize = descriptor->cellSize;
	Size winSize = descriptor->winSize;
	int i, j, nbins = descriptor->nbins;
	int rawBlockSize = blockSize.width * blockSize.height;

	nblocks = Size((winSize.width - blockSize.width) / blockStride.width + 1,
				   (winSize.height - blockSize.height) / blockStride.height + 1);
	ncells = Size(blockSize.width / cellSize.width, blockSize.height / cellSize.height);
	blockHistogramSize = ncells.width * ncells.height * nbins;

	if ( useCache ) {
		Size cacheSize((grad.cols - blockSize.width) / cacheStride.width + 1,
					   (winSize.height / cacheStride.height) + 1);
		blockCache.create(cacheSize.height, cacheSize.width * blockHistogramSize);
		blockCacheFlags.create(cacheSize);
		size_t i, cacheRows = blockCache.rows;
		ymaxCached.resize(cacheRows);
		for ( i = 0; i < cacheRows; i++ ) {
			ymaxCached[i] = -1;
		}
	}

	Mat_<float> weights(blockSize);
	float sigma = (float)descriptor->getWinSigma();
	float scale = 1.f / (sigma * sigma * 2);

	for (i = 0; i < blockSize.height; i++)
		for (j = 0; j < blockSize.width; j++) {
			float di = i - blockSize.height * 0.5f;
			float dj = j - blockSize.width * 0.5f;
			weights(i, j) = std::exp(-(di * di + dj * dj) * scale);
		}

	blockData.resize(nblocks.width * nblocks.height);
	pixData.resize(rawBlockSize * 3);

	// Initialize 2 lookup tables, pixData & blockData.
	// Here is why:
	//
	// The detection algorithm runs in 4 nested loops (at each pyramid layer):
	//  loop over the windows within the input image
	//    loop over the blocks within each window
	//      loop over the cells within each block
	//        loop over the pixels in each cell
	//
	// As each of the loops runs over a 2-dimensional array,
	// we could get 8(!) nested loops in total, which is very-very slow.
	//
	// To speed the things up, we do the following:
	//   1. loop over windows is unrolled in the HOGDescriptor::{compute|detect} methods;
	//         inside we compute the current search window using getWindow() method.
	//         Yes, it involves some overhead (function call + couple of divisions),
	//         but it's tiny in fact.
	//   2. loop over the blocks is also unrolled. Inside we use pre-computed blockData[j]
	//         to set up gradient and histogram pointers.
	//   3. loops over cells and pixels in each cell are merged
	//       (since there is no overlap between cells, each pixel in the block is processed once)
	//      and also unrolled. Inside we use PixData[k] to access the gradient values and
	//      update the histogram
	//
	count1 = count2 = count4 = 0;
	for ( j = 0; j < blockSize.width; j++ )
		for ( i = 0; i < blockSize.height; i++ ) {
			PixData* data = 0;
			float cellX = (j + 0.5f) / cellSize.width - 0.5f;
			float cellY = (i + 0.5f) / cellSize.height - 0.5f;
			int icellX0 = cvFloor(cellX);
			int icellY0 = cvFloor(cellY);
			int icellX1 = icellX0 + 1, icellY1 = icellY0 + 1;
			cellX -= icellX0;
			cellY -= icellY0;

			if ( (unsigned)icellX0 < (unsigned)ncells.width &&
					(unsigned)icellX1 < (unsigned)ncells.width ) {
				if ( (unsigned)icellY0 < (unsigned)ncells.height &&
						(unsigned)icellY1 < (unsigned)ncells.height ) {
					data = &pixData[rawBlockSize*2 + (count4++)];
					data->histOfs[0] = (icellX0 * ncells.height + icellY0) * nbins;
					data->histWeights[0] = (1.f - cellX) * (1.f - cellY);
					data->histOfs[1] = (icellX1 * ncells.height + icellY0) * nbins;
					data->histWeights[1] = cellX * (1.f - cellY);
					data->histOfs[2] = (icellX0 * ncells.height + icellY1) * nbins;
					data->histWeights[2] = (1.f - cellX) * cellY;
					data->histOfs[3] = (icellX1 * ncells.height + icellY1) * nbins;
					data->histWeights[3] = cellX * cellY;
				} else {
					data = &pixData[rawBlockSize + (count2++)];
					if ( (unsigned)icellY0 < (unsigned)ncells.height ) {
						icellY1 = icellY0;
						cellY = 1.f - cellY;
					}
					data->histOfs[0] = (icellX0 * ncells.height + icellY1) * nbins;
					data->histWeights[0] = (1.f - cellX) * cellY;
					data->histOfs[1] = (icellX1 * ncells.height + icellY1) * nbins;
					data->histWeights[1] = cellX * cellY;
					data->histOfs[2] = data->histOfs[3] = 0;
					data->histWeights[2] = data->histWeights[3] = 0;
				}
			} else {
				if ( (unsigned)icellX0 < (unsigned)ncells.width ) {
					icellX1 = icellX0;
					cellX = 1.f - cellX;
				}

				if ( (unsigned)icellY0 < (unsigned)ncells.height &&
						(unsigned)icellY1 < (unsigned)ncells.height ) {
					data = &pixData[rawBlockSize + (count2++)];
					data->histOfs[0] = (icellX1 * ncells.height + icellY0) * nbins;
					data->histWeights[0] = cellX * (1.f - cellY);
					data->histOfs[1] = (icellX1 * ncells.height + icellY1) * nbins;
					data->histWeights[1] = cellX * cellY;
					data->histOfs[2] = data->histOfs[3] = 0;
					data->histWeights[2] = data->histWeights[3] = 0;
				} else {
					data = &pixData[count1++];
					if ( (unsigned)icellY0 < (unsigned)ncells.height ) {
						icellY1 = icellY0;
						cellY = 1.f - cellY;
					}
					data->histOfs[0] = (icellX1 * ncells.height + icellY1) * nbins;
					data->histWeights[0] = cellX * cellY;
					data->histOfs[1] = data->histOfs[2] = data->histOfs[3] = 0;
					data->histWeights[1] = data->histWeights[2] = data->histWeights[3] = 0;
				}
			}
			data->gradOfs = (grad.cols * i + j) * 2;
			data->qangleOfs = (qangle.cols * i + j) * 2;
			data->gradWeight = weights(i, j);
		}

	assert( count1 + count2 + count4 == rawBlockSize );
	// defragment pixData
	for ( j = 0; j < count2; j++ ) {
		pixData[j + count1] = pixData[j + rawBlockSize];
	}
	for ( j = 0; j < count4; j++ ) {
		pixData[j + count1 + count2] = pixData[j + rawBlockSize*2];
	}
	count2 += count1;
	count4 += count2;

	// initialize blockData
	for ( j = 0; j < nblocks.width; j++ )
		for ( i = 0; i < nblocks.height; i++ ) {
			BlockData& data = blockData[j*nblocks.height + i];
			data.histOfs = (j * nblocks.height + i) * blockHistogramSize;
			data.imgOffset = Point(j * blockStride.width, i * blockStride.height);
		}
}


const float* HOGCache::getBlock(Point pt, float* buf) {
	float* blockHist = buf;
	assert(descriptor != 0);

	Size blockSize = descriptor->blockSize;
	pt += imgoffset;

	CV_Assert( (unsigned)pt.x <= (unsigned)(grad.cols - blockSize.width) &&
			   (unsigned)pt.y <= (unsigned)(grad.rows - blockSize.height) );

	if ( useCache ) {
		CV_Assert( pt.x % cacheStride.width == 0 &&
				   pt.y % cacheStride.height == 0 );
		Point cacheIdx(pt.x / cacheStride.width,
					   (pt.y / cacheStride.height) % blockCache.rows);
		if ( pt.y != ymaxCached[cacheIdx.y] ) {
			Mat_<uchar> cacheRow = blockCacheFlags.row(cacheIdx.y);
			cacheRow = (uchar)0;
			ymaxCached[cacheIdx.y] = pt.y;
		}

		blockHist = &blockCache[cacheIdx.y][cacheIdx.x*blockHistogramSize];
		uchar& computedFlag = blockCacheFlags(cacheIdx.y, cacheIdx.x);
		if ( computedFlag != 0 ) {
			return blockHist;
		}
		computedFlag = (uchar)1; // set it at once, before actual computing
	}

	int k, C1 = count1, C2 = count2, C4 = count4;
	const float* gradPtr = (const float*)(grad.data + grad.step * pt.y) + pt.x * 2;
	const uchar* qanglePtr = qangle.data + qangle.step * pt.y + pt.x * 2;

	CV_Assert( blockHist != 0 );

	for ( k = 0; k < blockHistogramSize; k++ ) {
		blockHist[k] = 0.f;
	}

	const PixData* _pixData = &pixData[0];

	for ( k = 0; k < C1; k++ ) {
		const PixData& pk = _pixData[k];
		const float* a = gradPtr + pk.gradOfs;
		float w = pk.gradWeight * pk.histWeights[0];
		const uchar* h = qanglePtr + pk.qangleOfs;
		int h0 = h[0], h1 = h[1];
		float* hist = blockHist + pk.histOfs[0];
		float t0 = hist[h0] + a[0] * w;
		float t1 = hist[h1] + a[1] * w;
		hist[h0] = t0; hist[h1] = t1;
	}

	for ( ; k < C2; k++ ) {
		const PixData& pk = _pixData[k];
		const float* a = gradPtr + pk.gradOfs;
		float w, t0, t1, a0 = a[0], a1 = a[1];
		const uchar* h = qanglePtr + pk.qangleOfs;
		int h0 = h[0], h1 = h[1];

		float* hist = blockHist + pk.histOfs[0];
		w = pk.gradWeight * pk.histWeights[0];
		t0 = hist[h0] + a0 * w;
		t1 = hist[h1] + a1 * w;
		hist[h0] = t0; hist[h1] = t1;

		hist = blockHist + pk.histOfs[1];
		w = pk.gradWeight * pk.histWeights[1];
		t0 = hist[h0] + a0 * w;
		t1 = hist[h1] + a1 * w;
		hist[h0] = t0; hist[h1] = t1;
	}

	for ( ; k < C4; k++ ) {
		const PixData& pk = _pixData[k];
		const float* a = gradPtr + pk.gradOfs;
		float w, t0, t1, a0 = a[0], a1 = a[1];
		const uchar* h = qanglePtr + pk.qangleOfs;
		int h0 = h[0], h1 = h[1];

		float* hist = blockHist + pk.histOfs[0];
		w = pk.gradWeight * pk.histWeights[0];
		t0 = hist[h0] + a0 * w;
		t1 = hist[h1] + a1 * w;
		hist[h0] = t0; hist[h1] = t1;

		hist = blockHist + pk.histOfs[1];
		w = pk.gradWeight * pk.histWeights[1];
		t0 = hist[h0] + a0 * w;
		t1 = hist[h1] + a1 * w;
		hist[h0] = t0; hist[h1] = t1;

		hist = blockHist + pk.histOfs[2];
		w = pk.gradWeight * pk.histWeights[2];
		t0 = hist[h0] + a0 * w;
		t1 = hist[h1] + a1 * w;
		hist[h0] = t0; hist[h1] = t1;

		hist = blockHist + pk.histOfs[3];
		w = pk.gradWeight * pk.histWeights[3];
		t0 = hist[h0] + a0 * w;
		t1 = hist[h1] + a1 * w;
		hist[h0] = t0; hist[h1] = t1;
	}

	normalizeBlockHistogram(blockHist);

	return blockHist;
}


void HOGCache::normalizeBlockHistogram(float* _hist) const {
	float* hist = &_hist[0];
	size_t i, sz = blockHistogramSize;

	float sum = 0;
	for ( i = 0; i < sz; i++ ) {
		sum += hist[i] * hist[i];
	}
	float scale = 1.f / (std::sqrt(sum) + sz * 0.1f), thresh = (float)descriptor->L2HysThreshold;
	for ( i = 0, sum = 0; i < sz; i++ ) {
		hist[i] = std::min(hist[i] * scale, thresh);
		sum += hist[i] * hist[i];
	}
	scale = 1.f / (std::sqrt(sum) + 1e-3f);
	for ( i = 0; i < sz; i++ ) {
		hist[i] *= scale;
	}
}


Size HOGCache::windowsInImage(Size imageSize, Size winStride) const {
	return Size((imageSize.width - winSize.width) / winStride.width + 1,
				(imageSize.height - winSize.height) / winStride.height + 1);
}

Rect HOGCache::getWindow(Size imageSize, Size winStride, int idx) const {
	int nwindowsX = (imageSize.width - winSize.width) / winStride.width + 1;
	int y = idx / nwindowsX;
	int x = idx - nwindowsX * y;
	return Rect( x * winStride.width, y * winStride.height, winSize.width, winSize.height );
}


void HOGDescriptor::compute(const Mat& img, vector<float>& descriptors,
							Size winStride, Size padding,
							const vector<Point>& locations) const {
	if ( winStride == Size() ) {
		winStride = cellSize;
	}
	Size cacheStride(gcd(winStride.width, blockStride.width),
					 gcd(winStride.height, blockStride.height));
	size_t nwindows = locations.size();
	padding.width = (int)alignSize(std::max(padding.width, 0), cacheStride.width);
	padding.height = (int)alignSize(std::max(padding.height, 0), cacheStride.height);
	Size paddedImgSize(img.cols + padding.width * 2, img.rows + padding.height * 2);

	HOGCache cache(this, img, padding, padding, nwindows == 0, cacheStride);

	if ( !nwindows ) {
		nwindows = cache.windowsInImage(paddedImgSize, winStride).area();
	}

	const HOGCache::BlockData* blockData = &cache.blockData[0];

	int nblocks = cache.nblocks.area();
	int blockHistogramSize = cache.blockHistogramSize;
	size_t dsize = getDescriptorSize();
	descriptors.resize(dsize * nwindows);

	for ( size_t i = 0; i < nwindows; i++ ) {
		float* descriptor = &descriptors[i*dsize];

		Point pt0;
		if ( !locations.empty() ) {
			pt0 = locations[i];
			if ( pt0.x < -padding.width || pt0.x > img.cols + padding.width - winSize.width ||
					pt0.y < -padding.height || pt0.y > img.rows + padding.height - winSize.height ) {
				continue;
			}
		} else {
			pt0 = cache.getWindow(paddedImgSize, winStride, (int)i).tl() - Point(padding);
			CV_Assert(pt0.x % cacheStride.width == 0 && pt0.y % cacheStride.height == 0);
		}

		for ( int j = 0; j < nblocks; j++ ) {
			const HOGCache::BlockData& bj = blockData[j];
			Point pt = pt0 + bj.imgOffset;

			float* dst = descriptor + bj.histOfs;
			const float* src = cache.getBlock(pt, dst);
			if ( src != dst )
				for ( int k = 0; k < blockHistogramSize; k++ ) {
					dst[k] = src[k];
				}
		}
	}
}


void HOGDescriptor::detect(const Mat& img,
						   vector<Point>& hits, double hitThreshold,
						   Size winStride, Size padding, const vector<Point>& locations) const {
	hits.clear();
	if ( svmDetector.empty() ) {
		return;
	}

	if ( winStride == Size() ) {
		winStride = cellSize;
	}
	Size cacheStride(gcd(winStride.width, blockStride.width),
					 gcd(winStride.height, blockStride.height));
	size_t nwindows = locations.size();
	padding.width = (int)alignSize(std::max(padding.width, 0), cacheStride.width);
	padding.height = (int)alignSize(std::max(padding.height, 0), cacheStride.height);
	Size paddedImgSize(img.cols + padding.width * 2, img.rows + padding.height * 2);

	HOGCache cache(this, img, padding, padding, nwindows == 0, cacheStride);

	if ( !nwindows ) {
		nwindows = cache.windowsInImage(paddedImgSize, winStride).area();
	}

	const HOGCache::BlockData* blockData = &cache.blockData[0];

	int nblocks = cache.nblocks.area();
	int blockHistogramSize = cache.blockHistogramSize;
	size_t dsize = getDescriptorSize();

	double rho = svmDetector.size() > dsize ? svmDetector[dsize] : 0;
	vector<float> blockHist(blockHistogramSize);

	for ( size_t i = 0; i < nwindows; i++ ) {
		Point pt0;
		if ( !locations.empty() ) {
			pt0 = locations[i];
			if ( pt0.x < -padding.width || pt0.x > img.cols + padding.width - winSize.width ||
					pt0.y < -padding.height || pt0.y > img.rows + padding.height - winSize.height ) {
				continue;
			}
		} else {
			pt0 = cache.getWindow(paddedImgSize, winStride, (int)i).tl() - Point(padding);
			CV_Assert(pt0.x % cacheStride.width == 0 && pt0.y % cacheStride.height == 0);
		}
		double s = rho;
		const float* svmVec = &svmDetector[0];
		int j, k;
		for ( j = 0; j < nblocks; j++, svmVec += blockHistogramSize ) {
			const HOGCache::BlockData& bj = blockData[j];
			Point pt = pt0 + bj.imgOffset;

			const float* vec = cache.getBlock(pt, &blockHist[0]);
			for ( k = 0; k <= blockHistogramSize - 4; k += 4 )
				s += vec[k] * svmVec[k] + vec[k+1] * svmVec[k+1] +
					 vec[k+2] * svmVec[k+2] + vec[k+3] * svmVec[k+3];
			for ( ; k < blockHistogramSize; k++ ) {
				s += vec[k] * svmVec[k];
			}
		}
		if ( s >= hitThreshold ) {
			hits.push_back(pt0);
		}
	}
}


struct HOGInvoker {
	HOGInvoker( const HOGDescriptor* _hog, const Mat& _img,
				double _hitThreshold, Size _winStride, Size _padding,
				const double* _levelScale, ConcurrentRectVector* _vec ) {
		hog = _hog;
		img = _img;
		hitThreshold = _hitThreshold;
		winStride = _winStride;
		padding = _padding;
		levelScale = _levelScale;
		vec = _vec;
	}

	void operator()( const BlockedRange& range ) const {
		int i, i1 = range.begin(), i2 = range.end();
		double minScale = i1 > 0 ? levelScale[i1] : i2 > 1 ? levelScale[i1+1] : std::max(img.cols, img.rows);
		Size maxSz(cvCeil(img.cols / minScale), cvCeil(img.rows / minScale));
		Mat smallerImgBuf(maxSz, img.type());
		vector<Point> locations;

		for ( i = i1; i < i2; i++ ) {
			double scale = levelScale[i];
			Size sz(cvRound(img.cols / scale), cvRound(img.rows / scale));
			Mat smallerImg(sz, img.type(), smallerImgBuf.data);
			if ( sz == img.size() ) {
				smallerImg = Mat(sz, img.type(), img.data, img.step);
			} else {
				resize(img, smallerImg, sz);
			}
			hog->detect(smallerImg, locations, hitThreshold, winStride, padding);
			Size scaledWinSize = Size(cvRound(hog->winSize.width * scale), cvRound(hog->winSize.height * scale));
			for ( size_t j = 0; j < locations.size(); j++ )
				vec->push_back(Rect(cvRound(locations[j].x * scale),
									cvRound(locations[j].y * scale),
									scaledWinSize.width, scaledWinSize.height));
		}
	}

	const HOGDescriptor* hog;
	Mat img;
	double hitThreshold;
	Size winStride;
	Size padding;
	const double* levelScale;
	ConcurrentRectVector* vec;
};


void HOGDescriptor::detectMultiScale(
	const Mat& img, vector<Rect>& foundLocations,
	double hitThreshold, Size winStride, Size padding,
	double scale0, int groupThreshold) const {
	double scale = 1.;
	const int maxLevels = 64;
	int levels = 0;

	vector<double> levelScale;
	for ( levels = 0; levels < maxLevels; levels++ ) {
		levelScale.push_back(scale);
		if ( cvRound(img.cols / scale) < winSize.width ||
				cvRound(img.rows / scale) < winSize.height ||
				scale0 <= 1 ) {
			break;
		}
		scale *= scale0;
	}
	levels = std::max(levels, 1);
	levelScale.resize(levels);

	ConcurrentRectVector allCandidates;

	parallel_for(BlockedRange(0, (int)levelScale.size()),
				 HOGInvoker(this, img, hitThreshold, winStride, padding, &levelScale[0], &allCandidates));

	foundLocations.resize(allCandidates.size());
	std::copy(allCandidates.begin(), allCandidates.end(), foundLocations.begin());

	groupRectangles(foundLocations, groupThreshold, 0.2);
}


typedef RTTIImpl<HOGDescriptor> HOGRTTI;

CvType hog_type( CV_TYPE_NAME_HOG_DESCRIPTOR, HOGRTTI::isInstance,
				 HOGRTTI::release, HOGRTTI::read, HOGRTTI::write, HOGRTTI::clone);

vector<float> HOGDescriptor::getDefaultPeopleDetector() {
	static const float detector[] = {
		0.05359386f, -0.14721455f, -0.05532170f, 0.05077307f,
		0.11547081f, -0.04268804f, 0.04635834f, -0.05468199f, 0.08232084f,
		0.10424068f, -0.02294518f, 0.01108519f, 0.01378693f, 0.11193510f,
		0.01268418f, 0.08528346f, -0.06309239f, 0.13054633f, 0.08100729f,
		-0.05209739f, -0.04315529f, 0.09341384f, 0.11035026f, -0.07596218f,
		-0.05517511f, -0.04465296f, 0.02947334f, 0.04555536f,
		-3.55954492e-003f, 0.07818956f, 0.07730991f, 0.07890715f, 0.06222893f,
		0.09001380f, -0.03574381f, 0.03414327f, 0.05677258f, -0.04773581f,
		0.03746637f, -0.03521175f, 0.06955440f, -0.03849038f, 0.01052293f,
		0.01736112f, 0.10867710f, 0.08748853f, 3.29739624e-003f, 0.10907028f,
		0.07913758f, 0.10393070f, 0.02091867f, 0.11594022f, 0.13182420f,
		0.09879354f, 0.05362710f, -0.06745391f, -7.01260753e-003f,
		5.24702156e-003f, 0.03236255f, 0.01407916f, 0.02207983f, 0.02537322f,
		0.04547948f, 0.07200756f, 0.03129894f, -0.06274468f, 0.02107014f,
		0.06035208f, 0.08636236f, 4.53164103e-003f, 0.02193363f, 0.02309801f,
		0.05568166f, -0.02645093f, 0.04448695f, 0.02837519f, 0.08975694f,
		0.04461516f, 0.08975355f, 0.07514391f, 0.02306982f, 0.10410084f,
		0.06368385f, 0.05943464f, 4.58420580e-003f, 0.05220337f, 0.06675851f,
		0.08358569f, 0.06712101f, 0.06559004f, -0.03930482f, -9.15936660e-003f,
		-0.05897915f, 0.02816453f, 0.05032348f, 0.06780671f, 0.03377650f,
		-6.09417039e-004f, -0.01795146f, -0.03083684f, -0.01302475f,
		-0.02972313f, 7.88706727e-003f, -0.03525961f, -2.50397739e-003f,
		0.05245084f, 0.11791293f, -0.02167498f, 0.05299332f, 0.06640524f,
		0.05190265f, -8.27316567e-003f, 0.03033127f, 0.05842173f,
		-4.01050318e-003f, -6.25105947e-003f, 0.05862958f, -0.02465461f,
		0.05546781f, -0.08228195f, -0.07234028f, 0.04640540f, -0.01308254f,
		-0.02506191f, 0.03100746f, -0.04665651f, -0.04591486f, 0.02949927f,
		0.06035462f, 0.02244646f, -0.01698639f, 0.01040041f, 0.01131170f,
		0.05419579f, -0.02130277f, -0.04321722f, -0.03665198f, 0.01126490f,
		-0.02606488f, -0.02228328f, -0.02255680f, -0.03427236f,
		-7.75165204e-003f, -0.06195229f, 8.21638294e-003f, 0.09535975f,
		-0.03709979f, -0.06942501f, 0.14579427f, -0.05448192f, -0.02055904f,
		0.05747357f, 0.02781788f, -0.07077577f, -0.05178314f, -0.10429011f,
		-0.11235505f, 0.07529039f, -0.07559302f, -0.08786739f, 0.02983843f,
		0.02667585f, 0.01382199f, -0.01797496f, -0.03141199f, -0.02098101f,
		0.09029204f, 0.04955018f, 0.13718739f, 0.11379953f, 1.80019124e-003f,
		-0.04577610f, -1.11108483e-003f, -0.09470536f, -0.11596080f,
		0.04489342f, 0.01784211f, 3.06850672e-003f, 0.10781866f,
		3.36498418e-003f, -0.10842580f, -0.07436839f, -0.10535070f,
		-0.01866805f, 0.16057891f, -5.07316366e-003f, -0.04295658f,
		-5.90488780e-003f, 8.82003549e-003f, -0.01492646f, -0.05029279f,
		-0.12875880f, 8.78831954e-004f, -0.01297184f, -0.07592774f,
		-0.02668831f, -6.93787413e-004f, 0.02406698f, -0.01773298f,
		-0.03855745f, -0.05877856f, 0.03259695f, 0.12826584f, 0.06292590f,
		-4.10733931e-003f, 0.10996531f, 0.01332991f, 0.02088735f, 0.04037504f,
		-0.05210760f, 0.07760046f, 0.06399347f, -0.05751930f, -0.10053057f,
		0.07505023f, -0.02139782f, 0.01796176f, 2.34400877e-003f, -0.04208319f,
		0.07355055f, 0.05093350f, -0.02996780f, -0.02219072f, 0.03355330f,
		0.04418742f, -0.05580705f, -0.05037573f, -0.04548179f, 0.01379514f,
		0.02150671f, -0.02194211f, -0.13682702f, 0.05464972f, 0.01608082f,
		0.05309116f, 0.04701022f, 1.33690401e-003f, 0.07575664f, 0.09625306f,
		8.92647635e-003f, -0.02819123f, 0.10866830f, -0.03439325f,
		-0.07092371f, -0.06004780f, -0.02712298f, -7.07467366e-003f,
		-0.01637020f, 0.01336790f, -0.10313606f, 0.04906582f, -0.05732445f,
		-0.02731079f, 0.01042235f, -0.08340668f, 0.03686501f, 0.06108340f,
		0.01322748f, -0.07809529f, 0.03774724f, -0.03413248f, -0.06096525f,
		-0.04212124f, -0.07982176f, -1.25973229e-003f, -0.03045501f,
		-0.01236493f, -0.06312395f, 0.04789570f, -0.04602066f, 0.08576570f,
		0.02521080f, 0.02988098f, 0.10314583f, 0.07060035f, 0.04520544f,
		-0.04426654f, 0.13146530f, 0.08386490f, 0.02164590f, -2.12280243e-003f,
		-0.03686353f, -0.02074944f, -0.03829959f, -0.01530596f, 0.02689708f,
		0.11867401f, -0.06043470f, -0.02785023f, -0.04775074f, 0.04878745f,
		0.06350956f, 0.03494788f, 0.01467400f, 1.17890188e-003f, 0.04379614f,
		2.03681854e-003f, -0.03958609f, -0.01072688f, 6.43705716e-003f,
		0.02996500f, -0.03418507f, -0.01960307f, -0.01219154f,
		-4.37000440e-003f, -0.02549453f, 0.02646318f, -0.01632513f,
		6.46516960e-003f, -0.01929734f, 4.78711911e-003f, 0.04962371f,
		0.03809111f, 0.07265724f, 0.05758125f, -0.03741554f, 0.01648608f,
		-8.45285598e-003f, 0.03996826f, -0.08185477f, 0.02638875f,
		-0.04026615f, -0.02744674f, -0.04071517f, 1.05096330e-003f,
		-0.04741232f, -0.06733172f, 8.70434940e-003f, -0.02192543f,
		1.35350740e-003f, -0.03056974f, -0.02975521f, -0.02887780f,
		-0.01210713f, -0.04828526f, -0.09066251f, -0.09969629f, -0.03665164f,
		-8.88111943e-004f, -0.06826669f, -0.01866150f, -0.03627640f,
		-0.01408288f, 0.01874239f, -0.02075835f, 0.09145175f, -0.03547291f,
		0.05396780f, 0.04198981f, 0.01301925f, -0.03384354f, -0.12201976f,
		0.06830920f, -0.03715654f, 9.55848210e-003f, 5.05685573e-003f,
		0.05659294f, 3.90764466e-003f, 0.02808490f, -0.05518097f, -0.03711621f,
		-0.02835565f, -0.04420464f, -0.01031947f, 0.01883466f,
		-8.49525444e-003f, -0.09419250f, -0.01269387f, -0.02133371f,
		-0.10190815f, -0.07844430f, 2.43644323e-003f, -4.09610150e-003f,
		0.01202551f, -0.06452291f, -0.10593818f, -0.02464746f, -0.02199699f,
		-0.07401930f, 0.07285886f, 8.87513801e-004f, 9.97662079e-003f,
		8.46779719e-003f, 0.03730333f, -0.02905126f, 0.03573337f, -0.04393689f,
		-0.12014472f, 0.03176554f, -2.76015815e-003f, 0.10824566f, 0.05090732f,
		-3.30179278e-003f, -0.05123822f, 5.04784798e-003f, -0.05664124f,
		-5.99415926e-003f, -0.05341901f, -0.01221393f, 0.01291318f,
		9.91760660e-003f, -7.56987557e-003f, -0.06193124f, -2.24549137e-003f,
		0.01987562f, -0.02018840f, -0.06975540f, -0.06601523f, -0.03349112f,
		-0.08910118f, -0.03371435f, -0.07406893f, -0.02248047f, -0.06159951f,
		2.77751544e-003f, -0.05723337f, -0.04792468f, 0.07518548f,
		2.77279224e-003f, 0.04211938f, 0.03100502f, 0.05278448f, 0.03954679f,
		-0.03006846f, -0.03851741f, -0.02792403f, -0.02875333f, 0.01531280f,
		0.02186953f, -0.01989829f, 2.50679464e-003f, -0.10258728f,
		-0.04785743f, -0.02887216f, 3.85063468e-003f, 0.01112236f,
		8.29218887e-003f, -0.04822981f, -0.04503597f, -0.03713100f,
		-0.06988008f, -0.11002295f, -2.69209221e-003f, 1.85383670e-003f,
		-0.05921049f, -0.06105053f, -0.08458050f, -0.04527602f,
		8.90329306e-004f, -0.05875023f, -2.68602883e-003f, -0.01591195f,
		0.03631859f, 0.05493166f, 0.07300330f, 5.53333294e-003f, 0.06400407f,
		0.01847740f, -5.76280477e-003f, -0.03210877f, 4.25160583e-003f,
		0.01166520f, -1.44864211e-003f, 0.02253744f, -0.03367080f, 0.06983195f,
		-4.22323542e-003f, -8.89401045e-003f, -0.07943393f, 0.05199728f,
		0.06065201f, 0.04133492f, 1.44032843e-003f, -0.09585235f, -0.03964731f,
		0.04232114f, 0.01750465f, -0.04487902f, -7.59733608e-003f, 0.02011171f,
		0.04673622f, 0.09011173f, -0.07869188f, -0.04682482f, -0.05080139f,
		-3.99383716e-003f, -0.05346331f, 0.01085723f, -0.03599333f,
		-0.07097908f, 0.03551549f, 0.02680387f, 0.03471529f, 0.01790393f,
		0.05471273f, 9.62048303e-003f, -0.03180215f, 0.05864431f, 0.02330614f,
		0.01633144f, -0.05616681f, -0.10245429f, -0.08302189f, 0.07291322f,
		-0.01972590f, -0.02619633f, -0.02485327f, -0.04627592f,
		1.48853404e-003f, 0.05514185f, -0.01270860f, -0.01948900f, 0.06373586f,
		0.05002292f, -0.03009798f, 8.76216311e-003f, -0.02474238f,
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		1.11975556e-003f, -0.01250656f, 2.86211423e-003f, 0.04333691f,
		-0.14603497f, -0.01946543f, -0.02327525f, -0.01973944f, 0.07944400f,
		-0.02224544f, -0.06701808f, 0.03476532f, 0.11505594f, -0.02712801f,
		-0.01665113f, 0.06315716f, -0.08205860f, 0.07431999f, 0.04915778f,
		-0.04468752f, -0.01490402f, 0.07400476f, -0.11650901f, 0.05102430f,
		0.04559118f, -0.05916039f, 0.08840760f, -0.01587902f, -0.14890194f,
		0.07857784f, 0.04710254f, -0.05381983f, -0.07331945f, -0.03604643f,
		0.15611970f, 0.07649943f, -0.05959348f, -0.02776607f, 0.11098688f,
		0.03758875f, -0.04446875f, 0.04933187f, 0.01345535f, 0.06921103f,
		0.07364785f, 0.05518956f, 0.02899585f, 0.09375840f, 0.10518434f,
		-0.04420241f, 0.01915282f, -3.56386811e-003f, 0.14586878f, 0.10286101f,
		-0.04360626f, -0.12723237f, 0.09076386f, 0.11119842f, -0.06035013f,
		0.09674817f, 0.08938243f, 0.07065924f, 0.02603180f, 5.84815582e-003f,
		-0.05922065f, 0.12360309f, 3.59695964e-003f, 2.99844006e-003f,
		0.03697936f, 0.02043072f, 0.04168725f, 0.01025975f, -0.01359980f,
		-0.01600920f, 0.02581056f, 0.02329250f, 2.98100687e-003f, 0.01629762f,
		0.06652115f, 0.05855627f, 0.01237463f, -0.01297135f, 0.01761587f,
		0.05090865f, 0.06549342f, -0.04425945f, 2.43203156e-003f,
		3.07327788e-003f, 0.06678630f, -0.04303836f, 0.01082393f, -0.06476044f,
		0.04077786f, 0.12441979f, 0.08237778f, 0.07424165f, 0.04065890f,
		0.06905543f, 0.09556347f, 0.12724875f, -0.02132082f, 0.08514154f,
		-0.04175328f, -0.02666954f, 0.01897836f, 0.03317382f, 9.45465732e-003f,
		-0.01238974f, -0.04242500f, -0.01419479f, -0.03545213f, -0.02440874f,
		0.08684119f, 0.04212951f, 0.02462858f, -0.01104825f, -5.01706870e-003f,
		0.02968982f, 0.02597476f, -0.01568939f, 0.04514892f, 0.06974549f,
		0.08670278f, 0.06828108f, 0.10238872f, 0.05405957f, 0.06548470f,
		-0.03763957f, 0.01366090f, 0.07069602f, 0.05363748f, 0.04798120f,
		0.11706422f, 0.05466456f, -0.01869259f, 0.06344382f, 0.03106543f,
		0.08432506f, -0.02061096f, 0.03821088f, -6.92190882e-003f,
		6.40467042e-003f, -0.01271779f, 6.89014705e-005f, 0.04541415f,
		-0.01899539f, -0.05020239f, 0.03000903f, 0.01090422f, 4.52452758e-003f,
		0.02573632f, -0.02388454f, -0.04200457f, 1.72783900e-003f,
		-0.05978370f, -0.02720562f, 0.06573715f, 0.01154317f, 0.01265615f,
		0.07375994f, -9.19828378e-003f, -0.04914120f, 0.02124831f, 0.06455322f,
		0.04372910f, -0.03310043f, 0.03605788f, -6.78055827e-003f,
		9.36202332e-003f, 0.01747596f, -0.06406314f, -0.06812935f, 0.08080816f,
		-0.02778088f, 0.02735260f, 0.06393493f, 0.06652229f, 0.05676993f,
		0.08640018f, -7.59188086e-003f, -0.02012847f, -0.04741159f,
		-0.01657069f, -0.01624399f, 0.05547778f, -2.33309763e-003f,
		0.01120033f, 0.06141156f, -0.06285004f, -0.08732341f, -0.09313398f,
		-0.04267832f, 5.57443965e-003f, 0.04809862f, 0.01773641f,
		5.37361018e-003f, 0.14842421f, -0.06298012f, -0.02935147f, 0.11443478f,
		-0.05034208f, 5.65494271e-003f, 0.02076526f, -0.04577984f,
		-0.04735741f, 0.02961071f, -0.09307127f, -0.04417921f, -0.04990027f,
		-0.03940028f, 0.01306016f, 0.06267900f, 0.03758737f, 0.08460117f,
		0.13858789f, 0.04862388f, -0.06319809f, -0.05655516f, 0.01885816f,
		-0.03285607f, 0.03371567f, -0.07040928f, -0.04514049f, 0.01392166f,
		0.08184422f, -0.07230316f, 0.02386871f, 0.02184591f, 0.02605764f,
		-0.01033954f, 9.29878280e-003f, 7.67351175e-003f, 0.15189242f,
		0.02069071f, -0.09738296f, -0.08894105f, -0.07768748f, 0.02332268f,
		-0.01778995f, -0.03258888f, -0.08180822f, -0.08492987f, 0.02290156f,
		-0.11368170f, -0.03554465f, -0.04533844f, -0.02861580f, 0.06782424f,
		0.01113123f, 0.02453644f, 0.12721945f, 0.08084814f, -0.03607795f,
		0.01109122f, 0.04803548f, -0.03489929f, 0.03399536f, -0.05682014f,
		8.59533902e-003f, -4.27904585e-003f, 0.03230887f, -0.01300198f,
		-0.01038137f, -0.07930113f, 8.33097473e-003f, 0.02296994f,
		-0.01306500f, -0.01881626f, 0.04413369f, 0.05729880f, -0.03761553f,
		0.01942326f, 1.64540811e-003f, -0.03811319f, 0.04190650f, -0.14978096f,
		-0.04514487f, 0.01209545f, -5.46460645e-003f, -0.01647195f,
		7.63064111e-003f, -0.07494587f, 0.08415288f, 0.10020141f, -0.01228561f,
		0.06553826f, 0.04554005f, 0.07890417f, 0.03041138f, 0.01752007f,
		0.09208256f, -3.74419295e-004f, 0.10549527f, 0.04686913f, 0.01894833f,
		-0.02651412f, -4.34682379e-003f, 5.44942822e-003f, 0.01444484f,
		0.05882156f, -0.03336544f, 0.04603891f, -0.10432546f, 0.01923928f,
		0.01842845f, -0.01712168f, -0.02222766f, 0.04693324f, -0.06202956f,
		-0.01422159f, 0.08732220f, -0.07706107f, 0.02661049f, -0.04300238f,
		-0.03092422f, -0.03552184f, -0.01886088f, -0.04979934f, 0.03906401f,
		0.04608644f, 0.04966111f, 0.04275464f, -0.04621769f, -0.02653212f,
		8.57011229e-003f, 0.03839684f, 0.05818764f, 0.03880796f,
		-2.76100676e-004f, 0.03076511f, -0.03266929f, -0.05374557f,
		0.04986527f, -9.45429131e-003f, 0.03582499f, -2.64564669e-003f,
		-1.07461517e-003f, 0.02962313f, -0.01483363f, 0.03060869f, 0.02448327f,
		0.01845641f, 0.03282966f, -0.03534438f, -0.01084059f, -0.01119136f,
		-1.85360224e-003f, -5.94652840e-004f, -0.04451817f, 2.98327743e-003f,
		0.06272484f, -0.02152076f, -3.05971340e-003f, -0.05070828f,
		0.01531762f, 0.01282815f, 0.05167150f, 9.46266949e-003f,
		-3.34558333e-003f, 0.11442288f, -0.03906701f, -2.67325155e-003f,
		0.03069184f, -0.01134165f, 0.02949462f, 0.02879886f, 0.03855566f,
		-0.03450781f, 0.09142872f, -0.02156654f, 0.06075062f, -0.06220816f,
		0.01944680f, 6.68372354e-003f, -0.06656796f, 8.70784000e-003f,
		0.03456013f, 0.02434320f, -0.13236357f, -0.04177035f, -0.02069627f,
		0.01068112f, 0.01505432f, -0.07517391f, -3.83571628e-003f,
		-0.06298508f, -0.02881260f, -0.13101046f, -0.07221562f,
		-5.79945277e-003f, -8.57300125e-003f, 0.03782469f, 0.02762164f,
		0.04942456f, -0.02936396f, 0.09597211f, 0.01921411f, 0.06101191f,
		-0.04787507f, -0.01379578f, -7.40224449e-003f, -0.02220136f,
		-0.01313756f, 7.77558051e-003f, 0.12296968f, 0.02939998f, 0.03594062f,
		-0.07788624f, -0.01133144f, 3.99316690e-004f, -0.06090347f,
		-0.01122066f, -4.68682544e-003f, 0.07633100f, -0.06748922f,
		-0.05640298f, -0.05265681f, -0.01139122f, -0.01624347f, -0.04715714f,
		-0.01099092f, 0.01048561f, 3.28499987e-003f, -0.05810167f,
		-0.07699911f, -0.03330683f, 0.04185145f, 0.03478536f, 0.02275165f,
		0.02304766f, 6.66040834e-003f, 0.10968148f, -5.93013782e-003f,
		-0.04858336f, -0.04203213f, -0.09316786f, -6.13074889e-003f,
		-0.02544625f, 0.01366201f, 9.18555818e-003f, -0.01846578f,
		-0.05622401f, -0.03989377f, -0.07810296f, 6.91275718e-003f,
		0.05957597f, -0.03901334f, 0.01572002f, -0.01193903f,
		-6.89400872e-003f, -0.03093356f, -0.04136098f, -0.01562869f,
		-0.04604580f, 0.02865234f, -0.08678447f, -0.03232484f, -0.05364593f,
		-0.01445016f, -0.07003860f, -0.08669746f, -0.04520775f, 0.04274122f,
		0.03117515f, 0.08175703f, 0.01081109f, 0.06379741f, 0.06199206f,
		0.02865988f, 0.02360346f, 0.06725410f, -0.03248780f, -9.37702879e-003f,
		0.08265898f, -0.02245839f, 0.05125763f, -0.01862395f, 0.01973453f,
		-0.01994494f, -0.10770868f, 0.03180375f, 3.23935156e-003f,
		-0.02142080f, -0.04256190f, 0.04760900f, 0.04282863f, 0.05635953f,
		-0.01870849f, 0.05540622f, -0.03042666f, 0.01455277f, -0.06630179f,
		-0.05843807f, -0.03739681f, -0.09739155f, -0.03220233f, -0.05620182f,
		-0.10381401f, 0.07400211f, 4.20676917e-003f, 0.03258535f,
		2.14308966e-003f, 0.05121966f, -0.01274337f, 0.02384761f, 0.06335578f,
		-0.07905591f, 0.08375625f, -0.07898903f, -0.06508528f, -0.02498444f,
		0.06535810f, 0.03970535f, 0.04895468f, -0.01169566f, -0.03980601f,
		0.05682293f, 0.05925463f, -0.01165808f, -0.07936699f, -0.04208954f,
		0.01333987f, 0.09051196f, 0.10098671f, -0.03974256f, 0.01238771f,
		-0.07501741f, -0.03655440f, -0.04301528f, 0.09216860f,
		4.63579083e-004f, 0.02851115f, 0.02142735f, 1.28244064e-004f,
		0.02879687f, -0.08554889f, -0.04838862f, 0.08135369f, -0.05756533f,
		0.01413900f, 0.03451880f, -0.06619488f, -0.03053130f, 0.02961676f,
		-0.07384635f, 0.01135692f, 0.05283910f, -0.07778034f, -0.02107482f,
		-0.05511716f, -0.13473752f, 0.03030157f, 0.06722020f, -0.06218817f,
		-0.05826827f, 0.06254654f, 0.02895772f, -0.01664000f, -0.03620280f,
		-0.01612278f, -1.46097376e-003f, 0.14013411f, -8.96181818e-003f,
		-0.03250246f, 3.38630192e-003f, 2.64779478e-003f, 0.03359732f,
		-0.02411991f, -0.04229729f, 0.10666174f, -6.66579151f
	};
	return vector<float>(detector, detector + sizeof(detector) / sizeof(detector[0]));
}

}
